| LDATS-package | Package to conduct two-stage analyses combining Latent Dirichlet Allocation with Bayesian Time Series models |
| AICc | Calculate AICc |
| autocorr_plot | Produce the autocorrelation panel for the TS diagnostic plot of a parameter |
| check_changepoints | Check that a set of change point locations is proper |
| check_control | Check that a control list is proper |
| check_document_covariate_table | Check that the document covariate table is proper |
| check_document_term_table | Check that document term table is proper |
| check_formula | Check that a formula is proper |
| check_formulas | Check that formulas vector is proper and append the response variable |
| check_LDA_models | Check that LDA model input is proper |
| check_LDA_set_inputs | Run a set of Latent Dirichlet Allocation models |
| check_LDA_TS_inputs | Run a full set of Latent Dirichlet Allocations and Time Series models |
| check_multinom_TS_inputs | Fit a multinomial change point Time Series model |
| check_nchangepoints | Check that nchangepoints vector is proper |
| check_seeds | Check that nseeds value or seeds vector is proper |
| check_timename | Check that the time vector is proper |
| check_topics | Check that topics vector is proper |
| check_TS_inputs | Conduct a single multinomial Bayesian Time Series analysis |
| check_TS_on_LDA_inputs | Conduct a set of Time Series analyses on a set of LDA models |
| check_weights | Check that weights vector is proper |
| conform_LDA_TS_data | Run a full set of Latent Dirichlet Allocations and Time Series models |
| count_trips | Count trips of the ptMCMC particles |
| diagnose_ptMCMC | Calculate ptMCMC summary diagnostics |
| document_weights | Calculate document weights for a corpus |
| ecdf_plot | Produce the posterior distribution ECDF panel for the TS diagnostic plot of a parameter |
| est_changepoints | Use ptMCMC to estimate the distribution of change point locations |
| est_regressors | Estimate the distribution of regressors, unconditional on the change point locations |
| eta_diagnostics_plots | Plot the diagnostics of the parameters fit in a TS model |
| eval_step | Conduct a within-chain step of the ptMCMC algorithm |
| expand_TS | Expand the TS models across the factorial combination of LDA models, formulas, and number of change points |
| iftrue | Replace if TRUE |
| jornada | Jornada rodent data |
| LDATS | Package to conduct two-stage analyses combining Latent Dirichlet Allocation with Bayesian Time Series models |
| LDA_msg | Create the model-running-message for an LDA |
| LDA_plot_bottom_panel | Plot the results of an LDATS LDA model |
| LDA_plot_top_panel | Plot the results of an LDATS LDA model |
| LDA_set | Run a set of Latent Dirichlet Allocation models |
| LDA_set_control | Create control list for set of LDA models |
| LDA_TS | Run a full set of Latent Dirichlet Allocations and Time Series models |
| LDA_TS_control | Create the controls list for the LDATS model |
| logLik.LDA_VEM | Calculate the log likelihood of a VEM LDA model fit |
| logLik.multinom_TS_fit | Log likelihood of a multinomial TS model |
| logLik.TS_fit | Determine the log likelihood of a Time Series model |
| logsumexp | Calculate the log-sum-exponential (LSE) of a vector |
| measure_eta_vcov | Summarize the regressor (eta) distributions |
| measure_rho_vcov | Summarize the rho distributions |
| memoise_fun | Logical control on whether or not to memoise |
| messageq | Optionally generate a message based on a logical input |
| mirror_vcov | Create a properly symmetric variance covariance matrix |
| modalvalue | Determine the mode of a distribution |
| multinom_TS | Fit a multinomial change point Time Series model |
| multinom_TS_chunk | Fit a multinomial Time Series model chunk |
| normalize | Normalize a vector |
| package_chunk_fits | Package the output of the chunk-level multinomial models into a multinom_TS_fit list |
| package_LDA_set | Package the output from LDA_set |
| package_LDA_TS | Package the output of LDA_TS |
| package_TS | Summarize the Time Series model |
| package_TS_on_LDA | Package the output of TS_on_LDA |
| plot.LDA_set | Plot a set of LDATS LDA models |
| plot.LDA_TS | Plot the key results from a full LDATS analysis |
| plot.LDA_VEM | Plot the results of an LDATS LDA model |
| plot.TS_fit | Plot an LDATS TS model |
| posterior_plot | Produce the posterior distribution histogram panel for the TS diagnostic plot of a parameter |
| pred_gamma_TS_plot | Create the summary plot for a TS fit to an LDA model |
| prep_chunks | Prepare the time chunk table for a multinomial change point Time Series model |
| prep_cpts | Initialize and update the change point matrix used in the ptMCMC algorithm |
| prep_ids | Initialize and update the chain ids throughout the ptMCMC algorithm |
| prep_LDA_control | Set the control inputs to include the seed |
| prep_pbar | Initialize and tick through the progress bar |
| prep_proposal_dist | Pre-calculate the change point proposal distribution for the ptMCMC algorithm |
| prep_ptMCMC_inputs | Prepare the inputs for the ptMCMC algorithm estimation of change points |
| prep_saves | Prepare and update the data structures to save the ptMCMC output |
| prep_temp_sequence | Prepare the ptMCMC temperature sequence |
| prep_TS_data | Prepare the model-specific data to be used in the TS analysis of LDA output |
| print.LDA_TS | Print the selected LDA and TS models of LDA_TS object |
| print.TS_fit | Print a Time Series model fit |
| print.TS_on_LDA | Print a set of Time Series models fit to LDAs |
| print_model_run_message | Print the message to the console about which combination of the Time Series and LDA models is being run |
| process_saves | Prepare and update the data structures to save the ptMCMC output |
| proposed_step_mods | Fit the chunk-level models to a time series, given a set of proposed change points within the ptMCMC algorithm |
| propose_step | Conduct a within-chain step of the ptMCMC algorithm |
| rho_diagnostics_plots | Plot the diagnostics of the parameters fit in a TS model |
| rho_hist | Create the summary plot for a TS fit to an LDA model |
| rho_lines | Add change point location lines to the time series plot |
| rodents | Portal rodent data |
| select_LDA | Select the best LDA model(s) for use in time series |
| select_TS | Select the best Time Series model |
| set_gamma_colors | Prepare the colors to be used in the gamma time series |
| set_LDA_plot_colors | Prepare the colors to be used in the LDA plots |
| set_LDA_TS_plot_cols | Create the list of colors for the LDATS summary plot |
| set_rho_hist_colors | Prepare the colors to be used in the change point histogram |
| set_TS_summary_plot_cols | Create the list of colors for the TS summary plot |
| sim_LDA_data | Simulate LDA data from an LDA structure given parameters |
| sim_LDA_TS_data | Simulate LDA_TS data from LDA and TS model structures and parameters |
| sim_TS_data | Simulate TS data from a TS model structure given parameters |
| softmax | Calculate the softmax of a vector or matrix of values |
| step_chains | Conduct a within-chain step of the ptMCMC algorithm |
| summarize_etas | Summarize the regressor (eta) distributions |
| summarize_rhos | Summarize the rho distributions |
| swap_chains | Conduct a set of among-chain swaps for the ptMCMC algorithm |
| take_step | Conduct a within-chain step of the ptMCMC algorithm |
| trace_plot | Produce the trace plot panel for the TS diagnostic plot of a parameter |
| TS | Conduct a single multinomial Bayesian Time Series analysis |
| TS_control | Create the controls list for the Time Series model |
| TS_diagnostics_plot | Plot the diagnostics of the parameters fit in a TS model |
| TS_on_LDA | Conduct a set of Time Series analyses on a set of LDA models |
| TS_summary_plot | Create the summary plot for a TS fit to an LDA model |
| update_cpts | Initialize and update the change point matrix used in the ptMCMC algorithm |
| update_ids | Initialize and update the chain ids throughout the ptMCMC algorithm |
| update_pbar | Initialize and tick through the progress bar |
| update_saves | Prepare and update the data structures to save the ptMCMC output |
| verify_changepoint_locations | Verify the change points of a multinomial time series model |